Connecting the “Dots”: From Free Radical Lipid Autoxidation to Cell Pathology and Disease
Bibliographic record
Abstract
Our understanding of lipid peroxidation in biology and medicine is rapidly evolving, as it is increasingly implicated in various diseases but also recognized as a key part of normal cell function, signaling, and death (ferroptosis). Not surprisingly, the root and consequences of lipid peroxidation have garnered increasing attention from multiple disciplines in recent years. Here we "connect the dots" between the fundamental chemistry underpinning the cascade reactions of lipid peroxidation (enzymatic or free radical), the reactive nature of the products formed (lipid-derived electrophiles), and the biological targets and mechanisms associated with these products that culminate in cellular responses. We additionally bring light to the use of highly sensitive, fluorescence-based methodologies. Stemming from the foundational concepts in chemistry and biology, these methodologies enable visualizing and quantifying each reaction in the cascade in a cellular and ultimately tissue context, toward deciphering the connections between the chemistry and physiology of lipid peroxidation. The review offers a platform in which the chemistry and biomedical research communities can access a comprehensive summary of fundamental concepts regarding lipid peroxidation, experimental tools for the study of such processes, as well as the recent discoveries by leading investigators with an emphasis on significant open questions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".